Update: 22 April, 2021 (T. French)

Personalysis Profiles

Respondents:  (n=44)

Amir A

Andrew B

Angelique R

Ari R

Ashley S

Bradley R

Caleb S

Chris K

Chuck L

Devendra M

Firouzeh M

Ford P

Funi C

Gino C

Jacob L

Jacob V

Jason M

Jessica Q

Jessica S

Jodi B

John M

Jonathn P

Kevin D

Keyana A

Laurel K

Linda D

Lizbeth M

Mauli M

Ngoc S

Pete F

Rocky R

Said S

Sarah N

Steve T

Susan H

Susan P

Thomas F

Thomas G

Tony R

Troy H

Vandana K

Vasyl A

Vickie H

Victoria A

Others

Personalysis Clustering

Clustering was used as a simple unsupervised learning technique seeking to cluster caregivers into homogeneous or similar subgroups (4) based on a combination of Personalysis dimension and color response scores.

Expedite (Red)

Contribution

Connection

Commitment

Explore (Blue)

Contribution

Connection

Commitment

Personalysis Teams

Below are 3 dimensional team views of all CA care givers with specific individuals called out for selected team. All care givers are clustered as well.

Adv. Analytics

Expedite (Red)

Clusters:

Explore (Blue)

Clusters:

Organize (Green)

Clusters:

Collaborate (Yellow)

Clusters:

Executives

Expedite (Red)

Clusters:

Explore (Blue)

Clusters:

Organize (Green)

Clusters:

Collaborate (Yellow)

Clusters:

CaOps, Data Eng, Adv Analytics

Expedite (Red)

Clusters:

Explore (Blue)

Clusters:

Organize (Green)

Clusters:

Collaborate (Yellow)

Clusters:


Clifton Strength Finder

var variables = ""
for (var name in this)
    variables += name + "\n";
console.log(variables)

/*
This could work too... but it's such a big unecessary code for something you could do in one line 
var split = variables.split("\n");
for (var i in split)
    console.log(split[i])
*/